Overview

Dataset statistics

Number of variables20
Number of observations18723
Missing cells74944
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 MiB
Average record size in memory607.3 B

Variable types

Numeric9
Categorical7
Unsupported4

Alerts

survey_id has constant value "1476"Constant
city has constant value "Amsterdam"Constant
name has a high cardinality: 18150 distinct valuesHigh cardinality
last_modified has a high cardinality: 18723 distinct valuesHigh cardinality
location has a high cardinality: 18723 distinct valuesHigh cardinality
room_id is highly overall correlated with reviewsHigh correlation
reviews is highly overall correlated with room_id and 1 other fieldsHigh correlation
overall_satisfaction is highly overall correlated with reviewsHigh correlation
accommodates is highly overall correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with accommodates and 1 other fieldsHigh correlation
price is highly overall correlated with accommodates and 1 other fieldsHigh correlation
latitude is highly overall correlated with neighborhoodHigh correlation
longitude is highly overall correlated with neighborhoodHigh correlation
neighborhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
room_type is highly imbalanced (52.9%)Imbalance
country has 18723 (100.0%) missing valuesMissing
borough has 18723 (100.0%) missing valuesMissing
bathrooms has 18723 (100.0%) missing valuesMissing
minstay has 18723 (100.0%) missing valuesMissing
name is uniformly distributedUniform
last_modified is uniformly distributedUniform
location is uniformly distributedUniform
room_id has unique valuesUnique
last_modified has unique valuesUnique
location has unique valuesUnique
country is an unsupported type, check if it needs cleaning or further analysisUnsupported
borough is an unsupported type, check if it needs cleaning or further analysisUnsupported
bathrooms is an unsupported type, check if it needs cleaning or further analysisUnsupported
minstay is an unsupported type, check if it needs cleaning or further analysisUnsupported
reviews has 2984 (15.9%) zerosZeros
overall_satisfaction has 5748 (30.7%) zerosZeros
bedrooms has 1154 (6.2%) zerosZeros

Reproduction

Analysis started2023-06-18 11:26:53.024779
Analysis finished2023-06-18 11:27:14.071455
Duration21.05 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

room_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct18723
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11205678
Minimum2818
Maximum20003728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:14.242056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2818
5-th percentile1018799.4
Q16050607.5
median12282874
Q316610843
95-th percentile19578785
Maximum20003728
Range20000910
Interquartile range (IQR)10560236

Descriptive statistics

Standard deviation6082192.3
Coefficient of variation (CV)0.54277771
Kurtosis-1.2261316
Mean11205678
Median Absolute Deviation (MAD)5236951
Skewness-0.25429781
Sum2.0980391 × 1011
Variance3.6993063 × 1013
MonotonicityNot monotonic
2023-06-18T16:57:14.445148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10176931 1
 
< 0.1%
7050078 1
 
< 0.1%
16448872 1
 
< 0.1%
11316029 1
 
< 0.1%
13561991 1
 
< 0.1%
10580396 1
 
< 0.1%
3957325 1
 
< 0.1%
8590437 1
 
< 0.1%
2180810 1
 
< 0.1%
14723562 1
 
< 0.1%
Other values (18713) 18713
99.9%
ValueCountFrequency (%)
2818 1
< 0.1%
3209 1
< 0.1%
20168 1
< 0.1%
25428 1
< 0.1%
25488 1
< 0.1%
27886 1
< 0.1%
28658 1
< 0.1%
28871 1
< 0.1%
29051 1
< 0.1%
29554 1
< 0.1%
ValueCountFrequency (%)
20003728 1
< 0.1%
19996091 1
< 0.1%
19995673 1
< 0.1%
19995327 1
< 0.1%
19995246 1
< 0.1%
19995106 1
< 0.1%
19994262 1
< 0.1%
19992677 1
< 0.1%
19992596 1
< 0.1%
19992241 1
< 0.1%

survey_id
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1476
18723 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters74892
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1476
2nd row1476
3rd row1476
4th row1476
5th row1476

Common Values

ValueCountFrequency (%)
1476 18723
100.0%

Length

2023-06-18T16:57:14.632629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T16:57:14.788876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1476 18723
100.0%

Most occurring characters

ValueCountFrequency (%)
1 18723
25.0%
4 18723
25.0%
7 18723
25.0%
6 18723
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18723
25.0%
4 18723
25.0%
7 18723
25.0%
6 18723
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18723
25.0%
4 18723
25.0%
7 18723
25.0%
6 18723
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18723
25.0%
4 18723
25.0%
7 18723
25.0%
6 18723
25.0%

host_id
Real number (ℝ)

Distinct15943
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35776117
Minimum2234
Maximum1.4183192 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:14.945103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2234
5-th percentile1477396.3
Q17140879
median19886414
Q352026801
95-th percentile1.2189162 × 108
Maximum1.4183192 × 108
Range1.4182968 × 108
Interquartile range (IQR)44885922

Descriptive statistics

Standard deviation37581026
Coefficient of variation (CV)1.0504501
Kurtosis0.49010902
Mean35776117
Median Absolute Deviation (MAD)15783470
Skewness1.2438812
Sum6.6983623 × 1011
Variance1.4123335 × 1015
MonotonicityNot monotonic
2023-06-18T16:57:15.132575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48703385 93
 
0.5%
113977564 88
 
0.5%
1464510 71
 
0.4%
107745142 64
 
0.3%
84453740 61
 
0.3%
65859990 54
 
0.3%
517215 52
 
0.3%
46691672 43
 
0.2%
84449589 37
 
0.2%
669178 36
 
0.2%
Other values (15933) 18124
96.8%
ValueCountFrequency (%)
2234 1
< 0.1%
3159 1
< 0.1%
3806 1
< 0.1%
5988 2
< 0.1%
7924 1
< 0.1%
12085 1
< 0.1%
20405 1
< 0.1%
34080 1
< 0.1%
36701 1
< 0.1%
40786 1
< 0.1%
ValueCountFrequency (%)
141831915 1
 
< 0.1%
141749109 1
 
< 0.1%
141747815 1
 
< 0.1%
141665148 4
< 0.1%
141658022 1
 
< 0.1%
141648682 1
 
< 0.1%
141551211 1
 
< 0.1%
141548705 1
 
< 0.1%
141542351 1
 
< 0.1%
141534602 1
 
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Entire home/apt
14978 
Private room
3682 
Shared room
 
63

Length

Max length15
Median length15
Mean length14.396571
Min length11

Characters and Unicode

Total characters269547
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShared room
2nd rowShared room
3rd rowShared room
4th rowShared room
5th rowShared room

Common Values

ValueCountFrequency (%)
Entire home/apt 14978
80.0%
Private room 3682
 
19.7%
Shared room 63
 
0.3%

Length

2023-06-18T16:57:15.351301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T16:57:15.523400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 14978
40.0%
home/apt 14978
40.0%
room 3745
 
10.0%
private 3682
 
9.8%
shared 63
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 33701
12.5%
t 33638
12.5%
o 22468
8.3%
r 22468
8.3%
a 18723
 
6.9%
18723
 
6.9%
m 18723
 
6.9%
i 18660
 
6.9%
h 15041
 
5.6%
p 14978
 
5.6%
Other values (7) 52424
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217123
80.6%
Space Separator 18723
 
6.9%
Uppercase Letter 18723
 
6.9%
Other Punctuation 14978
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33701
15.5%
t 33638
15.5%
o 22468
10.3%
r 22468
10.3%
a 18723
8.6%
m 18723
8.6%
i 18660
8.6%
h 15041
6.9%
p 14978
6.9%
n 14978
6.9%
Other values (2) 3745
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
E 14978
80.0%
P 3682
 
19.7%
S 63
 
0.3%
Space Separator
ValueCountFrequency (%)
18723
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 14978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 235846
87.5%
Common 33701
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33701
14.3%
t 33638
14.3%
o 22468
9.5%
r 22468
9.5%
a 18723
7.9%
m 18723
7.9%
i 18660
7.9%
h 15041
6.4%
p 14978
6.4%
E 14978
6.4%
Other values (5) 22468
9.5%
Common
ValueCountFrequency (%)
18723
55.6%
/ 14978
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33701
12.5%
t 33638
12.5%
o 22468
8.3%
r 22468
8.3%
a 18723
 
6.9%
18723
 
6.9%
m 18723
 
6.9%
i 18660
 
6.9%
h 15041
 
5.6%
p 14978
 
5.6%
Other values (7) 52424
19.4%

country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18723
Missing (%)100.0%
Memory size146.4 KiB

city
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Amsterdam
18723 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters168507
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmsterdam
2nd rowAmsterdam
3rd rowAmsterdam
4th rowAmsterdam
5th rowAmsterdam

Common Values

ValueCountFrequency (%)
Amsterdam 18723
100.0%

Length

2023-06-18T16:57:15.679963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-18T16:57:15.820579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
amsterdam 18723
100.0%

Most occurring characters

ValueCountFrequency (%)
m 37446
22.2%
A 18723
11.1%
s 18723
11.1%
t 18723
11.1%
e 18723
11.1%
r 18723
11.1%
d 18723
11.1%
a 18723
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149784
88.9%
Uppercase Letter 18723
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 37446
25.0%
s 18723
12.5%
t 18723
12.5%
e 18723
12.5%
r 18723
12.5%
d 18723
12.5%
a 18723
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 18723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168507
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 37446
22.2%
A 18723
11.1%
s 18723
11.1%
t 18723
11.1%
e 18723
11.1%
r 18723
11.1%
d 18723
11.1%
a 18723
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 37446
22.2%
A 18723
11.1%
s 18723
11.1%
t 18723
11.1%
e 18723
11.1%
r 18723
11.1%
d 18723
11.1%
a 18723
11.1%

borough
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18723
Missing (%)100.0%
Memory size146.4 KiB

neighborhood
Categorical

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
De Baarsjes / Oud West
3289 
De Pijp / Rivierenbuurt
2378 
Centrum West
2225 
Centrum Oost
1730 
Westerpark
1430 
Other values (18)
7671 

Length

Max length38
Median length23
Mean length17.515729
Min length6

Characters and Unicode

Total characters327947
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDe Pijp / Rivierenbuurt
2nd rowCentrum West
3rd rowWatergraafsmeer
4th rowCentrum West
5th rowDe Baarsjes / Oud West

Common Values

ValueCountFrequency (%)
De Baarsjes / Oud West 3289
17.6%
De Pijp / Rivierenbuurt 2378
12.7%
Centrum West 2225
11.9%
Centrum Oost 1730
9.2%
Westerpark 1430
7.6%
Noord-West / Noord-Midden 1418
7.6%
Oud Oost 1169
 
6.2%
Bos en Lommer 988
 
5.3%
Oostelijk Havengebied / Indische Buurt 921
 
4.9%
Watergraafsmeer 517
 
2.8%
Other values (13) 2658
14.2%

Length

2023-06-18T16:57:15.961191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8985
15.9%
de 5781
10.3%
west 5755
10.2%
oud 4952
 
8.8%
centrum 4054
 
7.2%
baarsjes 3289
 
5.8%
oost 3217
 
5.7%
rivierenbuurt 2378
 
4.2%
pijp 2378
 
4.2%
westerpark 1430
 
2.5%
Other values (27) 14130
25.1%

Most occurring characters

ValueCountFrequency (%)
e 41125
 
12.5%
37626
 
11.5%
r 24877
 
7.6%
s 22215
 
6.8%
t 22148
 
6.8%
u 17169
 
5.2%
d 14742
 
4.5%
o 14591
 
4.4%
i 12545
 
3.8%
a 11932
 
3.6%
Other values (33) 108977
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 229288
69.9%
Uppercase Letter 49212
 
15.0%
Space Separator 37626
 
11.5%
Other Punctuation 8985
 
2.7%
Dash Punctuation 2836
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41125
17.9%
r 24877
10.8%
s 22215
9.7%
t 22148
9.7%
u 17169
7.5%
d 14742
 
6.4%
o 14591
 
6.4%
i 12545
 
5.5%
a 11932
 
5.2%
n 11659
 
5.1%
Other values (13) 36285
15.8%
Uppercase Letter
ValueCountFrequency (%)
O 9253
18.8%
W 9135
18.6%
D 5823
11.8%
B 5644
11.5%
C 4054
8.2%
N 3906
7.9%
R 2378
 
4.8%
P 2378
 
4.8%
M 1418
 
2.9%
I 1299
 
2.6%
Other values (7) 3924
8.0%
Space Separator
ValueCountFrequency (%)
37626
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8985
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278500
84.9%
Common 49447
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41125
14.8%
r 24877
 
8.9%
s 22215
 
8.0%
t 22148
 
8.0%
u 17169
 
6.2%
d 14742
 
5.3%
o 14591
 
5.2%
i 12545
 
4.5%
a 11932
 
4.3%
n 11659
 
4.2%
Other values (30) 85497
30.7%
Common
ValueCountFrequency (%)
37626
76.1%
/ 8985
 
18.2%
- 2836
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41125
 
12.5%
37626
 
11.5%
r 24877
 
7.6%
s 22215
 
6.8%
t 22148
 
6.8%
u 17169
 
5.2%
d 14742
 
4.5%
o 14591
 
4.4%
i 12545
 
3.8%
a 11932
 
3.6%
Other values (33) 108977
33.2%

reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct284
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.741548
Minimum0
Maximum532
Zeros2984
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:16.148666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q317
95-th percentile67
Maximum532
Range532
Interquartile range (IQR)15

Descriptive statistics

Standard deviation33.52263
Coefficient of variation (CV)2.0023614
Kurtosis43.756435
Mean16.741548
Median Absolute Deviation (MAD)6
Skewness5.5027866
Sum313452
Variance1123.7667
MonotonicityNot monotonic
2023-06-18T16:57:16.351768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2984
 
15.9%
1 1510
 
8.1%
2 1246
 
6.7%
3 1103
 
5.9%
4 925
 
4.9%
5 876
 
4.7%
6 741
 
4.0%
7 683
 
3.6%
8 590
 
3.2%
9 529
 
2.8%
Other values (274) 7536
40.2%
ValueCountFrequency (%)
0 2984
15.9%
1 1510
8.1%
2 1246
6.7%
3 1103
 
5.9%
4 925
 
4.9%
5 876
 
4.7%
6 741
 
4.0%
7 683
 
3.6%
8 590
 
3.2%
9 529
 
2.8%
ValueCountFrequency (%)
532 1
< 0.1%
465 1
< 0.1%
463 1
< 0.1%
452 1
< 0.1%
447 1
< 0.1%
443 2
< 0.1%
433 1
< 0.1%
430 2
< 0.1%
425 1
< 0.1%
410 2
< 0.1%

overall_satisfaction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.301127
Minimum0
Maximum5
Zeros5748
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:16.508010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.2135575
Coefficient of variation (CV)0.67054601
Kurtosis-1.3171244
Mean3.301127
Median Absolute Deviation (MAD)0.5
Skewness-0.79270161
Sum61807
Variance4.8998369
MonotonicityNot monotonic
2023-06-18T16:57:16.648612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 7708
41.2%
0 5748
30.7%
4.5 4559
24.3%
4 577
 
3.1%
3.5 109
 
0.6%
3 19
 
0.1%
1.5 1
 
< 0.1%
2.5 1
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
0 5748
30.7%
1 1
 
< 0.1%
1.5 1
 
< 0.1%
2.5 1
 
< 0.1%
3 19
 
0.1%
3.5 109
 
0.6%
4 577
 
3.1%
4.5 4559
24.3%
5 7708
41.2%
ValueCountFrequency (%)
5 7708
41.2%
4.5 4559
24.3%
4 577
 
3.1%
3.5 109
 
0.6%
3 19
 
0.1%
2.5 1
 
< 0.1%
1.5 1
 
< 0.1%
1 1
 
< 0.1%
0 5748
30.7%

accommodates
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.922021
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:16.789219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q34
95-th percentile5
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3275239
Coefficient of variation (CV)0.45431703
Kurtosis14.340675
Mean2.922021
Median Absolute Deviation (MAD)0
Skewness2.3887979
Sum54709
Variance1.7623198
MonotonicityNot monotonic
2023-06-18T16:57:16.929836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 10024
53.5%
4 5579
29.8%
3 1585
 
8.5%
6 476
 
2.5%
5 471
 
2.5%
1 367
 
2.0%
8 105
 
0.6%
7 52
 
0.3%
16 20
 
0.1%
10 16
 
0.1%
Other values (6) 28
 
0.1%
ValueCountFrequency (%)
1 367
 
2.0%
2 10024
53.5%
3 1585
 
8.5%
4 5579
29.8%
5 471
 
2.5%
6 476
 
2.5%
7 52
 
0.3%
8 105
 
0.6%
9 8
 
< 0.1%
10 16
 
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 20
 
0.1%
14 6
 
< 0.1%
13 1
 
< 0.1%
12 10
 
0.1%
11 2
 
< 0.1%
10 16
 
0.1%
9 8
 
< 0.1%
8 105
0.6%
7 52
0.3%

bedrooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4303797
Minimum0
Maximum10
Zeros1154
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:17.086060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87901869
Coefficient of variation (CV)0.61453519
Kurtosis5.6257566
Mean1.4303797
Median Absolute Deviation (MAD)0
Skewness1.6013041
Sum26781
Variance0.77267386
MonotonicityNot monotonic
2023-06-18T16:57:17.211056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 11101
59.3%
2 4456
23.8%
3 1444
 
7.7%
0 1154
 
6.2%
4 473
 
2.5%
5 62
 
0.3%
6 19
 
0.1%
10 5
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
0 1154
 
6.2%
1 11101
59.3%
2 4456
23.8%
3 1444
 
7.7%
4 473
 
2.5%
5 62
 
0.3%
6 19
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
10 5
 
< 0.1%
9 2
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 19
 
0.1%
5 62
 
0.3%
4 473
 
2.5%
3 1444
 
7.7%
2 4456
23.8%
1 11101
59.3%

bathrooms
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18723
Missing (%)100.0%
Memory size146.4 KiB

price
Real number (ℝ)

Distinct423
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.59948
Minimum12
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:17.383390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile72
Q1108
median144
Q3192
95-th percentile330
Maximum6000
Range5988
Interquartile range (IQR)84

Descriptive statistics

Standard deviation108.94385
Coefficient of variation (CV)0.65392672
Kurtosis521.86526
Mean166.59948
Median Absolute Deviation (MAD)36
Skewness12.768987
Sum3119242
Variance11868.762
MonotonicityNot monotonic
2023-06-18T16:57:17.570867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 1023
 
5.5%
180 1001
 
5.3%
144 887
 
4.7%
150 621
 
3.3%
132 588
 
3.1%
108 562
 
3.0%
96 520
 
2.8%
114 509
 
2.7%
118 508
 
2.7%
240 495
 
2.6%
Other values (413) 12009
64.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
18 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 6
< 0.1%
25 1
 
< 0.1%
28 1
 
< 0.1%
29 2
 
< 0.1%
30 6
< 0.1%
ValueCountFrequency (%)
6000 1
< 0.1%
3770 1
< 0.1%
1920 1
< 0.1%
1799 1
< 0.1%
1558 1
< 0.1%
1428 1
< 0.1%
1412 1
< 0.1%
1386 1
< 0.1%
1343 1
< 0.1%
1319 1
< 0.1%

minstay
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18723
Missing (%)100.0%
Memory size146.4 KiB

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct18150
Distinct (%)97.2%
Missing52
Missing (%)0.3%
Memory size1.7 MiB
Amsterdam
 
36
Lovely apartment near Vondelpark
 
10
Beautiful apartment in Amsterdam
 
8
Cosy apartment in Amsterdam
 
8
Spacious family house with garden
 
8
Other values (18145)
18601 

Length

Max length78
Median length50
Mean length36.092336
Min length1

Characters and Unicode

Total characters673880
Distinct characters157
Distinct categories20 ?
Distinct scripts4 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17814 ?
Unique (%)95.4%

Sample

1st rowRed Light/ Canal view apartment (Shared)
2nd rowSunny and Cozy Living room in quite neighbours
3rd rowAmsterdam
4th rowCanal boat RIDE in Amsterdam
5th rowOne room for rent in a three room appartment

Common Values

ValueCountFrequency (%)
Amsterdam 36
 
0.2%
Lovely apartment near Vondelpark 10
 
0.1%
Beautiful apartment in Amsterdam 8
 
< 0.1%
Cosy apartment in Amsterdam 8
 
< 0.1%
Spacious family house with garden 8
 
< 0.1%
Magnificent panoramic city view 8
 
< 0.1%
Nice comfy room, magnificent view 7
 
< 0.1%
Lovely apartment in Amsterdam 7
 
< 0.1%
Spacious apartment near Vondelpark 7
 
< 0.1%
Cosy apartment near Vondelpark 6
 
< 0.1%
Other values (18140) 18566
99.2%
(Missing) 52
 
0.3%

Length

2023-06-18T16:57:17.792673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment 7118
 
6.7%
in 5730
 
5.4%
amsterdam 3588
 
3.4%
3195
 
3.0%
with 2669
 
2.5%
the 2165
 
2.0%
spacious 2082
 
2.0%
city 1906
 
1.8%
centre 1768
 
1.7%
room 1728
 
1.6%
Other values (4867) 73723
69.8%

Most occurring characters

ValueCountFrequency (%)
87491
 
13.0%
e 59230
 
8.8%
t 55217
 
8.2%
a 52626
 
7.8%
r 42831
 
6.4%
n 39759
 
5.9%
o 35472
 
5.3%
i 32482
 
4.8%
m 26379
 
3.9%
s 21195
 
3.1%
Other values (147) 221198
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 510398
75.7%
Space Separator 87492
 
13.0%
Uppercase Letter 54936
 
8.2%
Other Punctuation 11184
 
1.7%
Decimal Number 5572
 
0.8%
Dash Punctuation 1595
 
0.2%
Math Symbol 1136
 
0.2%
Close Punctuation 621
 
0.1%
Open Punctuation 588
 
0.1%
Other Symbol 236
 
< 0.1%
Other values (10) 122
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (22) 22
56.4%
Lowercase Letter
ValueCountFrequency (%)
e 59230
11.6%
t 55217
10.8%
a 52626
10.3%
r 42831
 
8.4%
n 39759
 
7.8%
o 35472
 
6.9%
i 32482
 
6.4%
m 26379
 
5.2%
s 21195
 
4.2%
p 19825
 
3.9%
Other values (20) 125382
24.6%
Uppercase Letter
ValueCountFrequency (%)
A 8892
16.2%
C 6863
12.5%
S 4399
 
8.0%
L 3283
 
6.0%
B 3251
 
5.9%
R 2791
 
5.1%
P 2694
 
4.9%
E 2341
 
4.3%
T 2219
 
4.0%
N 2194
 
4.0%
Other values (17) 16009
29.1%
Other Punctuation
ValueCountFrequency (%)
, 2817
25.2%
! 2756
24.6%
& 1686
15.1%
. 1473
13.2%
' 831
 
7.4%
/ 587
 
5.2%
@ 315
 
2.8%
" 285
 
2.5%
: 189
 
1.7%
* 154
 
1.4%
Other values (7) 91
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 1885
33.8%
1 992
17.8%
0 741
 
13.3%
5 498
 
8.9%
3 463
 
8.3%
4 412
 
7.4%
8 150
 
2.7%
6 150
 
2.7%
9 145
 
2.6%
7 136
 
2.4%
Other Symbol
ValueCountFrequency (%)
171
72.5%
33
 
14.0%
14
 
5.9%
5
 
2.1%
5
 
2.1%
° 3
 
1.3%
3
 
1.3%
1
 
0.4%
1
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 660
58.1%
| 460
40.5%
< 5
 
0.4%
> 4
 
0.4%
= 3
 
0.3%
~ 2
 
0.2%
1
 
0.1%
÷ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 614
98.9%
] 6
 
1.0%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 581
98.8%
[ 6
 
1.0%
1
 
0.2%
Space Separator
ValueCountFrequency (%)
87491
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1593
99.9%
2
 
0.1%
Nonspacing Mark
ValueCountFrequency (%)
15
93.8%
1
 
6.2%
Final Punctuation
ValueCountFrequency (%)
9
81.8%
2
 
18.2%
Control
ValueCountFrequency (%)
6
50.0%
6
50.0%
Currency Symbol
ValueCountFrequency (%)
4
80.0%
$ 1
 
20.0%
Initial Punctuation
ValueCountFrequency (%)
3
60.0%
2
40.0%
Other Number
ValueCountFrequency (%)
² 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 4
100.0%
Format
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 565334
83.9%
Common 108491
 
16.1%
Han 39
 
< 0.1%
Inherited 16
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
87491
80.6%
, 2817
 
2.6%
! 2756
 
2.5%
2 1885
 
1.7%
& 1686
 
1.6%
- 1593
 
1.5%
. 1473
 
1.4%
1 992
 
0.9%
' 831
 
0.8%
0 741
 
0.7%
Other values (56) 6226
 
5.7%
Latin
ValueCountFrequency (%)
e 59230
 
10.5%
t 55217
 
9.8%
a 52626
 
9.3%
r 42831
 
7.6%
n 39759
 
7.0%
o 35472
 
6.3%
i 32482
 
5.7%
m 26379
 
4.7%
s 21195
 
3.7%
p 19825
 
3.5%
Other values (47) 180318
31.9%
Han
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (22) 22
56.4%
Inherited
ValueCountFrequency (%)
15
93.8%
1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673492
99.9%
Misc Symbols 216
 
< 0.1%
None 64
 
< 0.1%
CJK 39
 
< 0.1%
Punctuation 34
 
< 0.1%
VS 16
 
< 0.1%
Dingbats 14
 
< 0.1%
Currency Symbols 4
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87491
 
13.0%
e 59230
 
8.8%
t 55217
 
8.2%
a 52626
 
7.8%
r 42831
 
6.4%
n 39759
 
5.9%
o 35472
 
5.3%
i 32482
 
4.8%
m 26379
 
3.9%
s 21195
 
3.1%
Other values (82) 220810
32.8%
Misc Symbols
ValueCountFrequency (%)
171
79.2%
33
 
15.3%
5
 
2.3%
5
 
2.3%
1
 
0.5%
1
 
0.5%
None
ValueCountFrequency (%)
² 22
34.4%
é 15
23.4%
´ 4
 
6.2%
à 4
 
6.2%
° 3
 
4.7%
É 3
 
4.7%
3
 
4.7%
2
 
3.1%
á 2
 
3.1%
  1
 
1.6%
Other values (5) 5
 
7.8%
VS
ValueCountFrequency (%)
15
93.8%
1
 
6.2%
Punctuation
ValueCountFrequency (%)
15
44.1%
9
26.5%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
Dingbats
ValueCountFrequency (%)
14
100.0%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
CJK
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (22) 22
56.4%
Math Operators
ValueCountFrequency (%)
1
100.0%

last_modified
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct18723
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2017-07-23 13:06:27.391699
 
1
2017-07-22 17:50:54.562470
 
1
2017-07-22 17:50:37.799843
 
1
2017-07-22 17:50:37.804073
 
1
2017-07-22 17:50:37.808374
 
1
Other values (18718)
18718 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters486798
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18723 ?
Unique (%)100.0%

Sample

1st row2017-07-23 13:06:27.391699
2nd row2017-07-23 13:06:23.607187
3rd row2017-07-23 13:06:23.603546
4th row2017-07-23 13:06:22.689787
5th row2017-07-23 13:06:19.681469

Common Values

ValueCountFrequency (%)
2017-07-23 13:06:27.391699 1
 
< 0.1%
2017-07-22 17:50:54.562470 1
 
< 0.1%
2017-07-22 17:50:37.799843 1
 
< 0.1%
2017-07-22 17:50:37.804073 1
 
< 0.1%
2017-07-22 17:50:37.808374 1
 
< 0.1%
2017-07-22 17:50:37.811917 1
 
< 0.1%
2017-07-22 17:50:41.366311 1
 
< 0.1%
2017-07-22 17:50:41.404879 1
 
< 0.1%
2017-07-22 17:50:44.108265 1
 
< 0.1%
2017-07-22 17:50:45.777034 1
 
< 0.1%
Other values (18713) 18713
99.9%

Length

2023-06-18T16:57:17.980153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-07-22 13694
36.6%
2017-07-23 5029
 
13.4%
13:06:07.443359 1
 
< 0.1%
13:06:22.689787 1
 
< 0.1%
13:06:19.681469 1
 
< 0.1%
13:06:19.663975 1
 
< 0.1%
13:06:09.988016 1
 
< 0.1%
13:06:09.984748 1
 
< 0.1%
13:05:45.744708 1
 
< 0.1%
13:06:07.452609 1
 
< 0.1%
Other values (18715) 18715
50.0%

Most occurring characters

ValueCountFrequency (%)
2 81800
16.8%
0 65421
13.4%
7 55572
11.4%
1 48766
10.0%
- 37446
7.7%
: 37446
7.7%
3 29626
 
6.1%
5 22515
 
4.6%
4 19641
 
4.0%
6 19288
 
4.0%
Other values (4) 69277
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 374460
76.9%
Other Punctuation 56169
 
11.5%
Dash Punctuation 37446
 
7.7%
Space Separator 18723
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 81800
21.8%
0 65421
17.5%
7 55572
14.8%
1 48766
13.0%
3 29626
 
7.9%
5 22515
 
6.0%
4 19641
 
5.2%
6 19288
 
5.2%
8 16269
 
4.3%
9 15562
 
4.2%
Other Punctuation
ValueCountFrequency (%)
: 37446
66.7%
. 18723
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 37446
100.0%
Space Separator
ValueCountFrequency (%)
18723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 486798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 81800
16.8%
0 65421
13.4%
7 55572
11.4%
1 48766
10.0%
- 37446
7.7%
: 37446
7.7%
3 29626
 
6.1%
5 22515
 
4.6%
4 19641
 
4.0%
6 19288
 
4.0%
Other values (4) 69277
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 81800
16.8%
0 65421
13.4%
7 55572
11.4%
1 48766
10.0%
- 37446
7.7%
: 37446
7.7%
3 29626
 
6.1%
5 22515
 
4.6%
4 19641
 
4.0%
6 19288
 
4.0%
Other values (4) 69277
14.2%

latitude
Real number (ℝ)

Distinct15595
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.365261
Minimum52.2962
Maximum52.42498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:18.152543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum52.2962
5-th percentile52.343288
Q152.355254
median52.364628
Q352.374797
95-th percentile52.389372
Maximum52.42498
Range0.12878
Interquartile range (IQR)0.019544

Descriptive statistics

Standard deviation0.015142042
Coefficient of variation (CV)0.00028916198
Kurtosis1.4182599
Mean52.365261
Median Absolute Deviation (MAD)0.009735
Skewness0.0079054175
Sum980434.77
Variance0.00022928145
MonotonicityNot monotonic
2023-06-18T16:57:18.371271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.354646 5
 
< 0.1%
52.360546 5
 
< 0.1%
52.361364 5
 
< 0.1%
52.366852 5
 
< 0.1%
52.355191 4
 
< 0.1%
52.354748 4
 
< 0.1%
52.36104 4
 
< 0.1%
52.361689 4
 
< 0.1%
52.362592 4
 
< 0.1%
52.361118 4
 
< 0.1%
Other values (15585) 18679
99.8%
ValueCountFrequency (%)
52.2962 1
< 0.1%
52.297203 1
< 0.1%
52.299763 1
< 0.1%
52.299846 1
< 0.1%
52.299875 1
< 0.1%
52.300105 1
< 0.1%
52.30013 1
< 0.1%
52.300915 1
< 0.1%
52.301257 1
< 0.1%
52.301683 1
< 0.1%
ValueCountFrequency (%)
52.42498 1
< 0.1%
52.424641 1
< 0.1%
52.424255 1
< 0.1%
52.423647 1
< 0.1%
52.423498 1
< 0.1%
52.423432 1
< 0.1%
52.423321 1
< 0.1%
52.422827 1
< 0.1%
52.422232 1
< 0.1%
52.422228 1
< 0.1%

longitude
Real number (ℝ)

Distinct17157
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8885852
Minimum4.763264
Maximum5.027689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.4 KiB
2023-06-18T16:57:18.574382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.763264
5-th percentile4.8453101
Q14.8643445
median4.885994
Q34.90748
95-th percentile4.9445407
Maximum5.027689
Range0.264425
Interquartile range (IQR)0.0431355

Descriptive statistics

Standard deviation0.034536882
Coefficient of variation (CV)0.0070648011
Kurtosis1.2170001
Mean4.8885852
Median Absolute Deviation (MAD)0.021585
Skewness0.53824711
Sum91528.98
Variance0.0011927962
MonotonicityNot monotonic
2023-06-18T16:57:18.761858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.907187 5
 
< 0.1%
4.888738 4
 
< 0.1%
4.904646 4
 
< 0.1%
4.856525 4
 
< 0.1%
4.893506 4
 
< 0.1%
4.875611 4
 
< 0.1%
4.893017 4
 
< 0.1%
4.86301 4
 
< 0.1%
4.877004 4
 
< 0.1%
4.861512 4
 
< 0.1%
Other values (17147) 18682
99.8%
ValueCountFrequency (%)
4.763264 1
< 0.1%
4.768452 1
< 0.1%
4.769151 1
< 0.1%
4.771083 1
< 0.1%
4.772725 1
< 0.1%
4.772822 1
< 0.1%
4.775168 1
< 0.1%
4.775748 1
< 0.1%
4.77647 1
< 0.1%
4.77764 1
< 0.1%
ValueCountFrequency (%)
5.027689 1
< 0.1%
5.026701 1
< 0.1%
5.015737 1
< 0.1%
5.013557 1
< 0.1%
5.013316 1
< 0.1%
5.013075 1
< 0.1%
5.012549 1
< 0.1%
5.011693 1
< 0.1%
5.011688 1
< 0.1%
5.011569 1
< 0.1%

location
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct18723
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0101000020E610000033FAD170CA8C13403BC5AA41982D4A40
 
1
0101000020E6100000C30DF8FC30821340342BDB87BC314A40
 
1
0101000020E61000007B849A2155B41340D6C9198A3B2E4A40
 
1
0101000020E610000056D3F544D7851340C79E3D97A9314A40
 
1
0101000020E61000002CB7B41A12A71340C5E40D30F32D4A40
 
1
Other values (18718)
18718 

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters936150
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18723 ?
Unique (%)100.0%

Sample

1st row0101000020E610000033FAD170CA8C13403BC5AA41982D4A40
2nd row0101000020E6100000842A357BA095134042791F4773304A40
3rd row0101000020E6100000A51133FB3CC613403543AA285E2B4A40
4th row0101000020E6100000DF180280638F134085EE92382B304A40
5th row0101000020E6100000CD902A8A57691340187B2FBE682F4A40

Common Values

ValueCountFrequency (%)
0101000020E610000033FAD170CA8C13403BC5AA41982D4A40 1
 
< 0.1%
0101000020E6100000C30DF8FC30821340342BDB87BC314A40 1
 
< 0.1%
0101000020E61000007B849A2155B41340D6C9198A3B2E4A40 1
 
< 0.1%
0101000020E610000056D3F544D7851340C79E3D97A9314A40 1
 
< 0.1%
0101000020E61000002CB7B41A12A71340C5E40D30F32D4A40 1
 
< 0.1%
0101000020E6100000EE7A698A0087134052F01472A5304A40 1
 
< 0.1%
0101000020E6100000CC24EA059F861340211D1EC2F8314A40 1
 
< 0.1%
0101000020E610000068791EDC9DC513403ECE3461FB2D4A40 1
 
< 0.1%
0101000020E6100000B9FC87F4DBD713406ADE718A8E304A40 1
 
< 0.1%
0101000020E61000002FFD4B5299D213402FC03E3A75314A40 1
 
< 0.1%
Other values (18713) 18713
99.9%

Length

2023-06-18T16:57:18.933711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0101000020e610000033fad170ca8c13403bc5aa41982d4a40 1
 
< 0.1%
0101000020e6100000fddcd0949d8e13404243ff04172f4a40 1
 
< 0.1%
0101000020e6100000df180280638f134085ee92382b304a40 1
 
< 0.1%
0101000020e6100000cd902a8a57691340187b2fbe682f4a40 1
 
< 0.1%
0101000020e6100000b090b932a896134060c8ea56cf2b4a40 1
 
< 0.1%
0101000020e61000005d70067fbfb813400b45ba9f53304a40 1
 
< 0.1%
0101000020e6100000dd09f65fe7761340d925aab706304a40 1
 
< 0.1%
0101000020e6100000459e245d33991340a439b2f2cb2c4a40 1
 
< 0.1%
0101000020e610000032c687d9cba613409fad8383bd2d4a40 1
 
< 0.1%
0101000020e6100000bc96900f7a961340e0d6dd3cd52b4a40 1
 
< 0.1%
Other values (18713) 18713
99.9%

Most occurring characters

ValueCountFrequency (%)
0 289829
31.0%
1 100408
 
10.7%
4 81235
 
8.7%
2 58034
 
6.2%
3 48096
 
5.1%
E 47519
 
5.1%
6 46156
 
4.9%
A 45108
 
4.8%
D 28544
 
3.0%
8 28194
 
3.0%
Other values (6) 163027
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 732898
78.3%
Uppercase Letter 203252
 
21.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289829
39.5%
1 100408
 
13.7%
4 81235
 
11.1%
2 58034
 
7.9%
3 48096
 
6.6%
6 46156
 
6.3%
8 28194
 
3.8%
7 28082
 
3.8%
9 27873
 
3.8%
5 24991
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
E 47519
23.4%
A 45108
22.2%
D 28544
14.0%
F 28123
13.8%
C 27427
13.5%
B 26531
13.1%

Most occurring scripts

ValueCountFrequency (%)
Common 732898
78.3%
Latin 203252
 
21.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289829
39.5%
1 100408
 
13.7%
4 81235
 
11.1%
2 58034
 
7.9%
3 48096
 
6.6%
6 46156
 
6.3%
8 28194
 
3.8%
7 28082
 
3.8%
9 27873
 
3.8%
5 24991
 
3.4%
Latin
ValueCountFrequency (%)
E 47519
23.4%
A 45108
22.2%
D 28544
14.0%
F 28123
13.8%
C 27427
13.5%
B 26531
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 936150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289829
31.0%
1 100408
 
10.7%
4 81235
 
8.7%
2 58034
 
6.2%
3 48096
 
5.1%
E 47519
 
5.1%
6 46156
 
4.9%
A 45108
 
4.8%
D 28544
 
3.0%
8 28194
 
3.0%
Other values (6) 163027
17.4%

Interactions

2023-06-18T16:57:11.448309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:56.586155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:58.801796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:00.764500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:02.625399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:04.785909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:06.608080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.175137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.791383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:11.641869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:56.906618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:58.999918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:01.027477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:02.889911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:04.996413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:06.791452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.357751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.983182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:11.814369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:57.288827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:59.210232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:01.226371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:03.130366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:05.229138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:06.960548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.553545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:10.168607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:12.211640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:57.516616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:59.418839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:01.444209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:03.304006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:05.411891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:07.139336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.727034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:10.351077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:12.380934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:57.752106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:59.615225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:01.661783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:03.477979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:05.611319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:07.306189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.889365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:10.535607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:12.559311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:57.948937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:59.843741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:01.861506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:03.672069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:05.806703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:07.489454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.085479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:10.733653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:12.732887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:58.151651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:00.096723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:02.060089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:03.929549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:05.997833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:07.656059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.255035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:10.913546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:12.894600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:58.352067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:00.259269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:02.257538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:04.147012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:06.191233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:07.824952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.420796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:11.083657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:13.076882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:56:58.568839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:00.512317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:02.436719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:04.566916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:06.422297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:08.004828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:09.603528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-18T16:57:11.279237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-06-18T16:57:19.089942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
room_idhost_idreviewsoverall_satisfactionaccommodatesbedroomspricelatitudelongituderoom_typeneighborhood
room_id1.0000.496-0.542-0.3500.007-0.021-0.016-0.0280.0100.0430.041
host_id0.4961.000-0.235-0.1770.004-0.023-0.050-0.0300.0110.0460.055
reviews-0.542-0.2351.0000.682-0.073-0.137-0.0880.070-0.0090.2050.020
overall_satisfaction-0.350-0.1770.6821.000-0.059-0.0620.0140.046-0.0110.1170.040
accommodates0.0070.004-0.073-0.0591.0000.7230.555-0.0210.0870.1980.070
bedrooms-0.021-0.023-0.137-0.0620.7231.0000.504-0.0450.0510.2180.086
price-0.016-0.050-0.0880.0140.5550.5041.000-0.0010.0590.0190.023
latitude-0.028-0.0300.0700.046-0.021-0.045-0.0011.000-0.1230.0830.696
longitude0.0100.011-0.009-0.0110.0870.0510.059-0.1231.0000.1080.693
room_type0.0430.0460.2050.1170.1980.2180.0190.0830.1081.0000.137
neighborhood0.0410.0550.0200.0400.0700.0860.0230.6960.6930.1371.000

Missing values

2023-06-18T16:57:13.376608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-18T16:57:13.808231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

room_idsurvey_idhost_idroom_typecountrycityboroughneighborhoodreviewsoverall_satisfactionaccommodatesbedroomsbathroomspriceminstaynamelast_modifiedlatitudelongitudelocation
010176931147649180562Shared roomNaNAmsterdamNaNDe Pijp / Rivierenbuurt74.521.0NaN156.0NaNRed Light/ Canal view apartment (Shared)2017-07-23 13:06:27.39169952.3562094.8874910101000020E610000033FAD170CA8C13403BC5AA41982D4A40
18935871147646718394Shared roomNaNAmsterdamNaNCentrum West454.541.0NaN126.0NaNSunny and Cozy Living room in quite neighbours2017-07-23 13:06:23.60718752.3785184.8961200101000020E6100000842A357BA095134042791F4773304A40
214011697147610346595Shared roomNaNAmsterdamNaNWatergraafsmeer10.031.0NaN132.0NaNAmsterdam2017-07-23 13:06:23.60354652.3388114.9435920101000020E6100000A51133FB3CC613403543AA285E2B4A40
3613797814768685430Shared roomNaNAmsterdamNaNCentrum West75.041.0NaN121.0NaNCanal boat RIDE in Amsterdam2017-07-23 13:06:22.68978752.3763194.8900280101000020E6100000DF180280638F134085EE92382B304A40
418630616147670191803Shared roomNaNAmsterdamNaNDe Baarsjes / Oud West10.021.0NaN93.0NaNOne room for rent in a three room appartment2017-07-23 13:06:19.68146952.3703844.8528730101000020E6100000CD902A8A57691340187B2FBE682F4A40
55790170147629968916Shared roomNaNAmsterdamNaNDe Pijp / Rivierenbuurt1844.521.0NaN102.0NaNBeautiful apartment2017-07-23 13:06:19.66397552.3422654.8971260101000020E6100000B090B932A896134060C8EA56CF2B4A40
693406014765037506Shared roomNaNAmsterdamNaNOostelijk Havengebied / Indische Buurt675.0161.0NaN462.0NaNLOTUS, Classic Dutch Saling Barge2017-07-23 13:06:09.98801652.3775524.9304180101000020E61000005D70067FBFB813400B45BA9F53304A40
7195900491476132687356Shared roomNaNAmsterdamNaNWesterpark20.021.0NaN414.0NaNbig boot Adam 042017-07-23 13:06:09.98474852.3752054.8661170101000020E6100000DD09F65FE7761340D925AAB706304A40
8502028014764059485Shared roomNaNAmsterdamNaNOud Oost20.021.0NaN222.0NaNBright modern appartment in East!2017-07-23 13:06:07.45260952.3573464.9128870101000020E610000032C687D9CBA613409FAD8383BD2D4A40
915810783147684978218Shared roomNaNAmsterdamNaNCentrum West00.0121.0NaN301.0NaNCANAL BOATTOUR AMSTERDAM covered boat 1,5 hour2017-07-23 13:06:07.44798952.3866104.8901280101000020E6100000FB03E5B67D8F13403D27BD6F7C314A40
room_idsurvey_idhost_idroom_typecountrycityboroughneighborhoodreviewsoverall_satisfactionaccommodatesbedroomsbathroomspriceminstaynamelast_modifiedlatitudelongitudelocation
187132763386147614122005Private roomNaNAmsterdamNaNSlotervaart1185.021.0NaN36.0NaNComfortable SKY ROOM 12th floor2017-07-22 16:05:14.17317552.3610434.8461340101000020E6100000792288F37062134091B932A8362E4A40
18714192032561476132265798Private roomNaNAmsterdamNaNBijlmer Centrum10.041.0NaN35.0NaNNEW Stylish room, Ziggodome, AFAS LIVE, ArenA, RAI2017-07-22 16:05:14.16879952.3200494.9556090101000020E6100000950D6B2A8BD213400A0F9A5DF7284A40
18715197341781476139135665Private roomNaNAmsterdamNaNOsdorp00.010.0NaN30.0NaNCozy Apartment in Nieuw-West2017-07-22 16:05:14.16641052.3567024.7923460101000020E61000003677F4BF5C2B13407A354069A82D4A40
1871628896714761501422Private roomNaNAmsterdamNaNDe Baarsjes / Oud West2815.031.0NaN36.0NaNBandB de Baarsjes Amsterdam2017-07-22 16:05:14.16397352.3619184.8555070101000020E61000000DFFE9060A6C1340B8EA3A54532E4A40
187171668538314765831960Private roomNaNAmsterdamNaNBos en Lommer55.021.0NaN30.0NaNA nice bed in the attic of my 'palace'.2017-07-22 16:05:14.16171452.3796384.8488290101000020E6100000E695EB6D33651340D0285DFA97304A40
1871817789893147647501089Private roomNaNAmsterdamNaNBijlmer Centrum105.031.0NaN32.0NaN1-3 pers. Cozy Rm AFAS Live, ArenA, ZIGGODOME2017-07-22 16:05:14.15896352.3197944.9556380101000020E6100000684293C492D2134080BA8102EF284A40
1871916877166147667093870Private roomNaNAmsterdamNaNBijlmer Centrum65.041.0NaN24.0NaNModern Room by Arena, ZIGGO, HmH2017-07-22 16:05:14.15198652.3190804.9548220101000020E61000005801BEDBBCD1134062670A9DD7284A40
1872019859427147629724632Private roomNaNAmsterdamNaNGeuzenveld / Slotermeer00.011.0NaN38.0NaNPrivate single room2017-07-22 16:05:14.14961052.3840284.8384030101000020E61000002079E750865A1340C85F5AD427314A40
18721171321641476115156569Private roomNaNAmsterdamNaNCentrum West134.521.0NaN36.0NaNCity Center studio in Touristic Amsterdam 12017-07-22 16:05:14.14618352.3721204.8909820101000020E6100000774CDD955D9013400118CFA0A12F4A40
187227605782147639503013Private roomNaNAmsterdamNaNCentrum West1134.521.0NaN35.0NaNI have a room available for rent2017-07-22 16:05:12.25705452.3813924.8996580101000020E6100000CD565EF23F9913405F7AFB73D1304A40